cff-version: 1.2.0 abstract: "<p><span style="background-color: rgb(255, 250, 234);">This collection contains all code to produce the results of </span><span style="color: rgb(51, 51, 51);">"Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee," </span><em style="color: rgb(51, 51, 51);">2021 IEEE International Conference on Robotics and Automation (ICRA)</em><span style="color: rgb(51, 51, 51);">, Xi'an, China, 2021, pp. 10243-10249, doi: 10.1109/ICRA48506.2021.9561440. </span>This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. To the best of our knowledge, this is the first DRL-based orientation estimation method using inertial sensors combined with a magnetometer. The code is written in Python. The packages used are listed in "requirements.txt". To reproduce the code, please refer to "README.md".</p>" authors: - family-names: Tang given-names: Yujie - family-names: Hu given-names: Liang - family-names: Zhou given-names: Zhipeng - family-names: Pan given-names: Wei title: "code underlying publication: Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee" keywords: version: 1 identifiers: - type: doi value: 10.4121/71bb6fd6-0983-442c-a266-fe3b7bee77e4.v1 license: MIT date-released: 2024-10-29